Comparison of Methods for Differential Gene Expression Using Proteomics Count Data
Bibliographic record
Abstract
The main goal of the thesis is to identify proteomics gene expression associated with certain experimental conditions or diseases. Many researchers have compared different statistical methods which identify differentially expressed genes. However, very few are relevant to proteomics datasets. The present research examines modeling, transformation, and normalization methods, selects certain leading packages with built-in methods for the proteomics datasets, and detects genes whose mean expressions differ among the treatment and control groups. Two methods, TweeDEseq and Limma-Voom, are recommended because they are superior to the other approaches regarding modeling the proteomics data and data manipulation. TweeDEseq, built on the Poisson-Tweedie model, is supposed to adapt any over-dispersion data. Although Limma-Voom is based on a negative binomial model, the Voom method can boost flexibility with its built-in function to generate a precision weight for each observation. Both methods perform a good trade-off between the statistical power and False Discovery Rate (FDR) control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".